From the course: Deep Learning with Python: Optimizing Deep Learning Models

Stochastic gradient descent (SGD) - Python Tutorial

From the course: Deep Learning with Python: Optimizing Deep Learning Models

Stochastic gradient descent (SGD)

“

- [Instructor] Stochastic gradient descent, or SGD, takes a different approach from batch gradient descent by computing gradients and updating parameters for each individual training example rather than the entire dataset. Think of SGD as taking quick small steps down the hill, adjusting your path based on immediate surroundings rather than considering the entire landscape. This method introduces a level of randomness or noise into the optimization process. One of the key benefits of stochastic gradient descent is that each update is quick because it processes only one sample at a time. This can significantly speed up the iteration process, allowing the model to start learning patterns from the data more rapidly. This immediacy can be particularly useful in online learning scenarios where data arrives in streams. SGD also requires less memory since it only needs to store a single data sample and the corresponding gradients. This makes it more suitable for situations where computational resources are limited or when dealing with extremely large datasets that cannot be loaded into memory all at once. An interesting advantage of the randomness in SGD is the ability to escape local minima. The noise in the updates can help the algorithm jump out of suboptimal solutions and potentially find a better global minimum. This makes SGD particularly useful in training deep learning models with complex loss surfaces where local minima are common obstacles. However, the high variance in updates is also one of SGD's significant limitations. Because each update is based on a single data point, the updates can fluctuate significantly. This can lead to a less stable convergence path, making it harder to predict when the model reached the minimum loss. The optimization path might resemble a zigzag pattern, potentially overshooting the minimum and requiring more iterations to converge. Another drawback is that SGD may require more iterations to converge compared to batch gradient descent. The noisy updates can cause the optimization process to take a more erratic path towards the minimum, potentially increasing the overall training time. This can be inefficient, especially when precise convergence is required. Additionally, processing one sample at a time limits opportunities for parallel computing. In modern computing environments where parallelization is key to efficiency, this can be a significant disadvantage. The inability to leverage multi-core processors or GPUs effectively means that SGD might not fully utilize available computational resources.

Contents